Digital Radiography (DR) detectors directly transform received exposure energy to digital image data. These detectors commonly contain an array of light sensitive picture elements, or pixels, arranged in a matrix of rows and columns and a scintillator, consisting of a material, such as gadolinium oxysulfide, Gd2O2S:Tb (GOS) or cesium iodide (CsI), that absorbs x-rays incident thereon and converts the x-ray energy to visible light photons. In some configurations, the scintillator is in direct contact with the light sensitive array. The array of light sensitive elements can be any type of solid state sensor, such as a flat panel detector, a charge-coupled device, or CMOS detector. The light sensitive material converts the incident light into electrical charge which is stored in the internal capacitance of each pixel. The magnitude of the stored electrical charge is related to the intensity of the excited light, which is, in turn, related to the intensity of the incident x-rays. The radiation image exposures captured on radiation-sensitive layers are converted, pixel by pixel, to electronic image data which is then stored in memory circuitry for subsequent read-out and display on suitable electronic image display devices.
Much like video sensors and other types of two-dimensional solid state image detectors, DR detectors include several thousands of picture elements, or pixels. Inevitably, some number of pixels is found to be defective. Compensation techniques such as defect mapping and corrective image processing allow the use of DR detectors having defective pixels, provided that such pixels can be detected (e.g., number is relatively small) and proper steps taken for correcting the image.
Defect mapping and correction procedures are commonly coupled with gain and offset calibration, which compensate for pixel-to-pixel variations in sensitivity and dark current. The most basic calibration and correction algorithms generally include two steps as taught by James A. Seibert, John M. Boone, and Karen K. Lindfors in “Flat-field correction technique for digital detectors,” Proc. SPIE Vol. 3336, 1998, p. 348-354. First, the dark signal of the detector (that is, the signal in the absence of any x-ray exposure) is obtained. Pixel by pixel variations in the dark signal of the detector are characterized to form a dark or offset map containing the dark variations. The offset map is then subtracted from the x-ray exposure in a process termed dark or offset correction. Secondly, variations in the sensitivity of the pixels are characterized. This is done by capturing one or more flat field exposures, which are then offset-corrected. The resulting image is the gain map. In the gain correction step, the offset-corrected x-ray exposure is divided by the gain map. Ideally, this two-step procedure compensates for any fixed pattern noise introduced by the detector.
Defect identification methods often explore anomalies in the gain and offset maps produced during calibration, for example by identifying pixels with gain and offset values that differ significantly from their surroundings, and by setting upper and lower thresholds for allowable values in gain and offset maps, to update the defect maps for a given detector.
Examples for this type of defect identification are given in ASTM Standard E2597, “Standard Practice for Manufacturing Characterization of Digital Detector Arrays” (2008) and U.S. Pat. No. 7,602,951 by Hsieh et al., “Method and system for providing defective cell correction in a medical imaging device”. Thus, periodic recalibration can help to manage defective pixels with related art DR detectors and can help to produce corrected images with few, if any, visible defective pixels.
Related art DR detectors generally accumulate few additional defective pixels over time and require infrequent recalibration. These detectors are often permanently mounted on a wall stand, in an examination table or some type of gantry or other type of adjustable framework that provides a secure mechanical mount (e.g., or tether) for positioning the detector behind the patient and at a proper disposition with respect to the x-ray source.
Because of normal and rough handling of portable radiographic detectors, the required intervals between calibration procedures, needed for maintaining suitable image quality, are less predictable for fully portable detectors. One solution would be simply to require more frequent calibration for these units. Calibration could thus be required, for example, after a certain number of images were taken. However, this type of arbitrary interval negatively impacts productivity. Most calibration procedures require x-ray exposures and therefore radiology staff time and attention and each calibration reduces the overall utilization time of the DR detector.
Clearly, there is a need to monitor the calibration state of the detector during regular clinical operation and to alert the user when calibration is needed, but calibration effectively disrupts operator workflow and increases access time for obtaining the current fully corrected clinical image. This disruption and time loss may be unacceptable in many clinical environments. In critical situations, such as in the emergency room or intensive care unit, for example, valuable time would be lost. Methods have been disclosed for updating defects exclusively from dark images, which can be done without operator intervention and while the detector is idle. See for example, May et al. in U.S. Pat. No. 6,693,668 titled “Self-diagnostic image sensor” or U.S. Pat. No. 7,362,916 by Yamazaki, entitled “Radiation imaging method, radiation imaging apparatus, computer program and computer-readable recording medium”. Other methods have been disclosed that identify new defects directly from radiographic images, which can also be done without operator involvement. See for example, US patent application US20070165934A1 by Maac and Kloessner entitled “Device and method for correcting defects in x-ray images” or U.S. Pat. No. 6,919,568 by Odogba et al., entitled “Method and apparatus for identifying composite defective pixel map”. However, these methods are generally more computationally intensive and not 100% reliable because the defects have to be identified within the image content. This overhead affects every captured image and may increase image access time.
In summary, while there are some indications that related art pixel defect detection methods may perform well enough when used within more permanent DR detector installations, these same methods do not appear to successfully address particular requirements and workflow of the portable DR detector. There is, thus, a need for improved defect identification and correction for portable DR detectors.